New word identification is a difficult point in Chinese word segmentation processing. In the automatic word segmentation processing of large Chinese texts, new word can cause segmentation mistakes. The paper defines new word identification as a binary classification problem: whether a character sequence in certain context is a new word or not, and use two statistical learning approaches based on support vector machine (SVM) and C4.5. We then investigate various linguistic and statistical features including Independent Word Probability of former character, Independent Word Probability of latter character, front position In-word probability of former character, back position In-word probability of latter character, Mutual Information and frequency. In PK-close test of the 1st Special Interest Group for Chinese Language Processing (SIGHAN) bakeoff, this approach achieves the high precision and recall.